SoroushVahidi/scidocs huggingface dataset:
Optimizing distributions over molecular space . An Objective-Reinforced Generative Adversarial Network for Inverse-design Chemistry ( ORGANIC )
(2017)

Molecular discovery seeks to generate chemical species tailored to very specific needs. In this paper, we present ORGANIC, a framework based on Objective-Reinforced Generative Adversarial Networks (ORGAN), capable of producing a distribution over molecular space that matches with a certain set of desirable metrics. This methodology combines two successful techniques from the machine learning community: a Generative Adversarial Network (GAN), to create non-repetitive sensible molecular species, and Reinforcement Learning (RL), to bias this generative distribution towards certain attributes. We explore several applications, from optimization of random physicochemical properties to candidates for drug discovery and organic photovoltaic material design. 1
Opportunities and Challenges of Switched Reluctance Motor Drives for Electric Propulsion: A Comparative Study
(2017)

Selection of the proper electric traction drive is an important step in design and performance optimization of electrified powertrains. Due to the use of high energy magnets, permanent magnet synchronous machines (PMSM) have been the primary choice in the electric traction motor market. However, manufacturers are very interested to find a permanent magnet-free alternative as a fallback option due to unstable cost of rare-earth metals and fault tolerance issues related to the constant permanent magnet excitation. In this paper, a new comprehensive review of electric machines (EMs) that includes various new switched reluctance machine topologies in addition to conventional EMs such as PMSM, induction machine, synchronous reluctance machine (SynRel), and PM-assisted SynRel is presented. This paper is based on performances such as power density, efficiency, torque ripple, vibration and noise, and fault tolerance. These systematic examinations prove that recently proposed magnetic configurations such as double-stator switched reluctance machine can be a reasonable substitute for permanent magnet machines in electric traction applications.
Supervised Learning of Semantics-Preserving Hashing via Deep Neural Networks for Large-Scale Image Search
(2015)

This paper presents a supervised deep hashing approach that constructs binary hash codes from labeled data for large-scale image search. We assume that semantic labels are governed by a set of latent attributes in which each attribute can be on or off, and classification relies on these attributes. Based on this assumption, our approach, dubbed supervised semanticspreserving deep hashing (SSDH), constructs hash functions as a latent layer in a deep network in which binary codes are learned by the optimization of an objective function defined over classification error and other desirable properties of hash codes. With this design, SSDH has a nice property that classification and retrieval are unified in a single learning model, and the learned binary codes not only preserve the semantic similarity between images but also are efficient for image search. Moreover, SSDH performs joint learning of image representations, hash codes, and classification in a pointwised manner and thus is naturally scalable to large-scale datasets. SSDH is simple and can be easily realized by a slight modification of an existing deep architecture for classification; yet it is effective and outperforms other unsupervised and supervised hashing approaches on several benchmarks and one large dataset comprising more than 1 million images.
Bank Customer Churn Prediction Based on Support Vector Machine: Taking a Commercial Bank's VIP Customer Churn as the Example
(2008)

Customer churn analysis and prediction play an important role in customer relationship management and improve benefit of enterprise. According to the bank's customer churn data which is large scale and imbalance, this paper presented a support vector machine model to predict customer churn. The method was compared with artificial neural network, decision tree, logistic regression and naive Bayesian classifier regarding customer churn prediction for a commercial bank's VIP customers. It is found that the method has the best accuracy rate, hit rate, covering rate and lift coefficient, and provides an effective measurement for bank's customer churn prediction.
Unsupervised Image-to-Image Translation Networks
(2017)

Most of the existing image-to-image translation frameworks—mapping an image in one domain to a corresponding image in another—are based on supervised learning, i.e., pairs of corresponding images in two domains are required for learning the translation function. This largely limits their applications, because capturing corresponding images in two different domains is often a difficult task. To address the issue, we propose the UNsupervised Image-to-image Translation (UNIT) framework, which is based on variational autoencoders and generative adversarial networks. The proposed framework can learn the translation function without any corresponding images in two domains. We enable this learning capability by combining a weight-sharing constraint and an adversarial training objective. Through visualization results from various unsupervised image translation tasks, we verify the effectiveness of the proposed framework. An ablation study further reveals the critical design choices. Moreover, we apply the UNIT framework to the unsupervised domain adaptation task and achieve better results than competing algorithms do in benchmark datasets.
Process scheduling under uncertainty: Review and challenges
(2008)

Uncertainty is a very important concern in production scheduling since it can cause infeasibilities and production disturbances. Thus scheduling nder uncertainty has received a lot of attention in the open literature in recent years from chemical engineering and operations research communities. he purpose of this paper is to review the main methodologies that have been developed to address the problem of uncertainty in production cheduling as well as to identify the main challenges in this area. The uncertainties in process scheduling are first analyzed, and the different athematical approaches that exist to describe process uncertainties are classified. Based on the different descriptions for the uncertainties, lternative scheduling approaches and relevant optimization models are reviewed and discussed. Further research challenges in the field of process cheduling under uncertainty are identified and some new ideas are discussed. 2007 Elsevier Ltd. All rights reserved.
Diagnosis and compensation of amplitude imbalance, imperfect quadrant and offset in resolver signals
(2016)

In this paper, a new method for detecting some errors in resolver signals is presented. Sin and cosine signals in resolver to digital converter's (RDC's) input might have errors, such as amplitude imbalance, imperfect quadrant or DC offset. Like other sensors some defects in mechanical structure and electrical or magnetic circuits cause to these errors in resolver output signals. The amplitude and phase of resolver output signals and consequently calculated angle are affected by these errors at any moment. Resolver output signals are more affected in their peak points than the other points. These peak points are easier and more accurate than the other points for analyzing, too. It is obvious that any error has unique effects on the signals. Therefore, by analyzing the effect on signals' peak points the error type and the value can be detected. To avoid wrong detection, error signal is defined, which detects the per unit signals deviation from the unit circle. Also, for error detection, there is no need to interrupt converter's operation. The proposed method is simulated in Matlab/Simulink environment, and the obtained results validate its effectiveness. Also, it is simulated on TMS320F2812 DSP using CCS 3.3, and the results are presented. Furthermore, by implementing the present RDC on motor controller DSP, it will be a low-cost approach.
A Key Characteristic of Sex Differences in the Developing Brain: Greater Variability in Brain Structure of Boys than Girls.
(2018)

In many domains, including cognition and personality, greater variability is observed in males than in females in humans. However, little is known about how variability differences between sexes are represented in the brain. The present study tested whether there is a sex difference in variance in brain structure using a cohort of 643 males and 591 females aged between 3 and 21 years. The broad age-range of the sample allowed us to test if variance differences in the brain differ across age. We observed significantly greater male than female variance for several key brain structures, including cerebral white matter and cortex, hippocampus, pallidum, putamen, and cerebellar cortex volumes. The differences were observed at both upper and lower extremities of the distributions and appeared stable across development. These findings move beyond mean levels by showing that sex differences were pronounced for variability, thereby providing a novel perspective on sex differences in the developing brain.
Apposcopy: semantics-based detection of Android malware through static analysis
(2014)

We present Apposcopy, a new semantics-based approach for identifying a prevalent class of Android malware that steals private user information. Apposcopy incorporates (i) a high-level language for specifying signatures that describe semantic characteristics of malware families and (ii) a static analysis for deciding if a given application matches a malware signature. The signature matching algorithm of Apposcopy uses a combination of static taint analysis and a new form of program representation called Inter-Component Call Graph to efficiently detect Android applications that have certain control- and data-flow properties. We have evaluated Apposcopy on a corpus of real-world Android applications and show that it can effectively and reliably pinpoint malicious applications that belong to certain malware families.
Towards 3D object maps for autonomous household robots
(2007)

This paper describes a mapping system that acquires 3D object models of man-made indoor environments such as kitchens. The system segments and geometrically reconstructs cabinets with doors, tables, drawers, and shelves, objects that are important for robots retrieving and manipulating objects in these environments. The system also acquires models of objects of daily use such glasses, plates, and ingredients. The models enable the recognition of the objects in cluttered scenes and the classification of newly encountered objects. Key technical contributions include (1) a robust, accurate, and efficient algorithm for constructing complete object models from 3D point clouds constituting partial object views, (2) feature-based recognition procedures for cabinets, tables, and other task-relevant furniture objects, and (3) automatic inference of object instance and class signatures for objects of daily use that enable robots to reliably recognize the objects in cluttered and real task contexts. We present results from the sensor-based mapping of a real kitchen.
Redundancy in Perceptual and Linguistic Experience: Comparing Feature-Based and Distributional Models of Semantic Representation
(2011)

Since their inception, distributional models of semantics have been criticized as inadequate cognitive theories of human semantic learning and representation. A principal challenge is that the representations derived by distributional models are purely symbolic and are not grounded in perception and action; this challenge has led many to favor feature-based models of semantic representation. We argue that the amount of perceptual and other semantic information that can be learned from purely distributional statistics has been underappreciated. We compare the representations of three feature-based and nine distributional models using a semantic clustering task. Several distributional models demonstrated semantic clustering comparable with clustering-based on feature-based representations. Furthermore, when trained on child-directed speech, the same distributional models perform as well as sensorimotor-based feature representations of children's lexical semantic knowledge. These results suggest that, to a large extent, information relevant for extracting semantic categories is redundantly coded in perceptual and linguistic experience. Detailed analyses of the semantic clusters of the feature-based and distributional models also reveal that the models make use of complementary cues to semantic organization from the two data streams. Rather than conceptualizing feature-based and distributional models as competing theories, we argue that future focus should be on understanding the cognitive mechanisms humans use to integrate the two sources.
Designing an UHF RFID Reader Antenna
(2014)

In this paper, a circularly polarized (CP) Octagonal shape microstrip patch antenna fed by coaxial feed is designed and analyzed for RFID (Radio Frequency Identification) reader applications. The physical parameters of the structure as well as its partial ground plane are analyzed and optimized using Zealand IE3D simulation software. Return loss (S11), voltage standing wave ratio (VSWR), directivity and gain are carried out. The results show that the proposed antenna has good impedance and radiation characteristics over the required bandwidth, 860-960 MHz (UHF RFID band). The return loss of the optimized Octagonal-shaped microstrip patch antenna is below 10dB over the UHF frequency band. The proposed antenna is very promising for various modern communication applications.
Emotion AI , Real-Time Emotion Detection using CNN
(2016)

In this paper, we describe a Convolutional Neural Network (CNN) approach to realtime emotion detection. We utilize data from the Extended Cohn-Kanade dataset, Japanese Female Facial Expression data set, and our own custom images in order to train this model, and apply preprocessing steps to improve performance. We re-train a LeNet and AlexNet implementation, both of which perform with above 97% accuracy. Qualitative analysis of real-time images shows that the above models perform reasonably well at classifying facial expressions, but not as well as the quantitative results would indicate.
Identity-Based Broadcast Encryption with Constant Size Ciphertexts and Private Keys
(2007)

This paper describes the first identity-based broadcast encryption scheme (IBBE) with constant size ciphertexts and private keys. In our scheme, the public key is of size linear in the maximal size m of the set of receivers, which is smaller than the number of possible users (identities) in the system. Compared with a recent broadcast encryption system introduced by Boneh, Gentry and Waters (BGW), our system has comparable properties, but with a better efficiency: the public key is shorter than in BGW. Moreover, the total number of possible users in the system does not have to be fixed in the setup.
Unobservable Communication over Fully Untrusted Infrastructure
(2016)

Keeping communication private has become increasingly important in an era of mass surveillance and statesponsored attacks. While hiding the contents of a conversation has well-known solutions, hiding the associated metadata (participants, duration, etc.) remains a challenge, especially if one cannot trust ISPs or proxy servers. This paper describes a communication system called Pung that provably hides all content and metadata while withstanding global adversaries. Pung is a key-value store where clients deposit and retrieve messages without anyone— including Pung’s servers—learning of the existence of a conversation. Pung is based on private information retrieval, which we make more practical for our setting with new techniques. These include a private multi-retrieval scheme, an application of the power of two choices, and batch codes. These extensions allow Pung to handle 103× more users than prior systems with a similar threat model.
IRON: Functional Encryption using Intel SGX
(2017)

Functional encryption (FE) is an extremely powerful cryptographic mechanism that lets an authorized entity compute on encrypted data, and learn the results in the clear. However, all current cryptographic instantiations for general FE are too impractical to be implemented. We construct IRON, a provably secure, and practical FE system using Intel's recent Software Guard Extensions (SGX). We show that IRON can be applied to complex functionalities, and even for simple functions, outperforms the best known cryptographic schemes. We argue security by modeling FE in the context of hardware elements, and prove that IRON satisfies the security model.
SCONE: Secure Linux Containers with Intel SGX
(2016)

Sergei Arnautov, Bohdan Trach, Franz Gregor, Thomas Knauth, Andre Martin, Christian Priebe, Joshua Lind, Divya Muthukumaran, Dan O’Keeffe, Mark L Stillwell, David Goltzsche, David Eyers, Rüdiger Kapitza, Peter Pietzuch, and Christof Fetzer Fakultät Informatik, TU Dresden, christof.fetzer@tu-dresden.de Dept. of Computing, Imperial College London, prp@imperial.ac.uk Informatik, TU Braunschweig, rrkapitz@ibr.cs.tu-bs.de Dept. of Computer Science, University of Otago, dme@cs.otago.ac.nz
Sanctum: Minimal Hardware Extensions for Strong Software Isolation
(2016)

Sanctum offers the same promise as Intel’s Software Guard Extensions (SGX), namely strong provable isolation of software modules running concurrently and sharing resources, but protects against an important class of additional software attacks that infer private information from a program’s memory access patterns. Sanctum shuns unnecessary complexity, leading to a simpler security analysis. We follow a principled approach to eliminating entire attack surfaces through isolation, rather than plugging attack-specific privacy leaks. Most of Sanctum’s logic is implemented in trusted software, which does not perform cryptographic operations using keys, and is easier to analyze than SGX’s opaque microcode, which does. Our prototype targets a Rocket RISC-V core, an open implementation that allows any researcher to reason about its security properties. Sanctum’s extensions can be adapted to other processor cores, because we do not change any major CPU building block. Instead, we add hardware at the interfaces between generic building blocks, without impacting cycle time. Sanctum demonstrates that strong software isolation is achievable with a surprisingly small set of minimally invasive hardware changes, and a very reasonable overhead.
Consensus photometric stereo
(2010)

This paper describes a photometric stereo method that works with a wide range of surface reflectances. Unlike previous approaches that assume simple parametric models such as Lambertian reflectance, the only assumption that we make is that the reflectance has three properties; monotonicity, visibility, and isotropy with respect to the cosine of light direction and surface orientation. In fact, these properties are observed in many non-Lambertian diffuse reflectances. We also show that the monotonicity and isotropy properties hold specular lobes with respect to the cosine of the surface orientation and the bisector between the light direction and view direction. Each of these three properties independently gives a possible solution space of the surface orientation. By taking the intersection of the solution spaces, our method determines the surface orientation in a consensus manner. Our method naturally avoids the need for radiometrically calibrating cameras because the radiometric response function preserves these three properties. The effectiveness of the proposed method is demonstrated using various simulated and real-world scenes that contain a variety of diffuse and specular surfaces.
A computer vision-aided motion sensing algorithm for mobile robot's indoor navigation
(2016)

This paper presents the design and analysis of a computer vision-aided motion sensing algorithm for wheeled mobile robot's indoor navigation. The algorithm is realized using two vision cameras attached on a wheeled mobile robot. The first camera is positioned at front-looking direction while the second camera is positioned at downward-looking direction. An algorithm is developed to process the images acquired from the cameras to yield the mobile robot's positions and orientations. The proposed algorithm is implemented on a wheeled mobile robot for real-world effectiveness testing. Results are compared and shown the accuracy of the proposed algorithm. At the end of the paper, an artificial landmark approach is introduced to improve the navigation efficiency. Future work involved implementing the proposed artificial landmark for indoor navigation applications with minimized accumulated errors.
ANALYTIC PROBLEMS FOR ELLIPTIC CURVES
(2005)

We consider some problems of analytic number theory for elliptic curves which can be considered as analogues of classical questions around the distribution of primes in arithmetic progressions to large moduli, and to the question of twin primes. This leads to some local results on the distribution of the group structures of elliptic curves defined over a prime finite field, exhibiting an interesting dichotomy for the occurence of the possible groups.
Classification of text documents supervised by domain ontologies
(2012)

The research objective is to establish an approach for supporting the classification of text documents referring to a specified domain. The focus is on the preliminary topic assignment to the documents used for training the model. The method implements domain ontology as background knowledge. The idea consists in extracting the preliminary topics for training the classifier by means of unsupervised machine learning on a text corpus and further alignment of the document vectors to concepts of the ontology. The results obtained by classification of new documents supervised by e-governance ontology with several machine learning algorithms showed sufficient match of their content to the ontology concepts. A conclusion is drawn that the approach can support the automatic extraction of documents relevant to any domain described by ontology.
Gene selection for cancer identification: a decision tree model empowered by particle swarm optimization algorithm
(2013)

In the application of microarray data, how to select a small number of informative genes from thousands of genes that may contribute to the occurrence of cancers is an important issue. Many researchers use various computational intelligence methods to analyzed gene expression data. To achieve efficient gene selection from thousands of candidate genes that can contribute in identifying cancers, this study aims at developing a novel method utilizing particle swarm optimization combined with a decision tree as the classifier. This study also compares the performance of our proposed method with other well-known benchmark classification methods (support vector machine, self-organizing map, back propagation neural network, C4.5 decision tree, Naive Bayes, CART decision tree, and artificial immune recognition system) and conducts experiments on 11 gene expression cancer datasets. Based on statistical analysis, our proposed method outperforms other popular classifiers for all test datasets, and is compatible to SVM for certain specific datasets. Further, the housekeeping genes with various expression patterns and tissue-specific genes are identified. These genes provide a high discrimination power on cancer classification.